Distribution-based Adversarial Filter Feature Selection against Evasion Attack

Abstract

Feature selection plays an important role in machine learning in order to reduce model complexity and extract more meaningful information. The recent studies indicate that not only the generalization ability but also the security should be considered in selecting features in an adversarial environment in which a classifier may be misled by an adversary intentionally. However, since only the nearest legitimate sample to a malicious sample is considered, the existing adversarial filter feature selection method is sensitive to outlier samples and is not suitable for Boolean features. A distribution-based adversarial filter feature selection method is proposed against evasion attack in this study. Our method uses distribution-based measurements, such as Symmetric Uncertainty and Earth Mover's Distance, are used to quantify the generalization ability and security of a feature subset. The experiments suggest our proposed method outperforms existing methods in terms of robustness and stability in datasets with Boolean and real-valued features.

Lab Members

Patrick Chan

Associate Professor, Vice Dean

Patrick Chan works on machine learning, deep learning, image processing, adversarial learning, and secure machine learning.